Artificial Intelligence

How smart is AI today? And will it eventually be smarter than us?

AI is used in various areas already, an increasing number of different applications fall back on the potential of the technology. Do we have to be afraid of AI? An overview on the current state of the technology and an outlook into the year 2030. 

The current technological situation

Today, weak artificial intelligence is being used with increasing frequency to tackle specialised problems, such as when IBM’s Watson is put to work analysing millions of cancer studies in a matter of minutes in order to diagnose a patient’s rare illness. Strong artificial intelligence, which is just as smart or even smarter than human beings does not yet exist. But the quantity of digital data is growing all the same, and with this, the breeding ground for AI. In an increasing number of contexts, algorithms are recognising patterns and correlations, calculating the likelihood of specific future scenarios (predictive analysis). But trying to use deep learning to train neural networks for individual tasks is still comparatively complex.

What does this mean for us in the present?

Big data and artificial intelligence may not really be taking over the whole world, but we as a (preferably global) society must nevertheless come to some agreement about just what these algorithms, data and programmers will be allowed to decide. Because automation is increasingly responsible for determining who will suffer what consequences, who will receive support and who won’t, for example, it is of enormous importance that these processes be designed transparently. Big data and AI can make all of our lives better, they can lead to completely new insights, and they represent tremendous potential for development aid as well. But civil society must fight to ensure that this power of data doesn’t stay in the hands of a few corporations bent on profit. 

How does AI look like in 2030?

The future technological situation

In 2030, the use of weak AI will be continually expanding to new areas, and for every conceivable problem there’ll be an algorithm rummaging through what for humans would be impenetrable masses of data. The necessary supercomputers NGOs, which in any case will already have their own data analysis departments. An ordinary high school diploma will be all that’s required in most cases to be able to train neural networks. In the social sector and in development aid, not only will measures be decided upon using predictive probability calculations, but past eff orts will also be retrospectively evaluated. A neural network will have proposed a draft of the Digital Development Goals – successors to the SDGs – that offers the highest probability that the goals will be reached. 

What will this mean for us in 2030? 

Along with the IoT, big data and AI have coalesced to become the greatest and most complex machine humankind has ever known. This machine, to which everything is connected, is now beyond anyone's comprehension. Legislation and regulation can't keep up with the breakneck pace of development. Legal and technological requirements will have been created to give the people control over their own data. In the wild west of Silicon Valley, data cowboys fire off predictions like there's no tomorrow. True, there's less poverty around the world, but the global ecosystem is on its last legs, and the inequitable distribution of wealth is on the rise. In Germany, the Analogue Social Party (ASP) wins 17% of the vote in the federal elections. 

Conclusion

AI is on the rise. Yet, there is no reason for human kind to be afraid, as strong AI does not exist currently and will not exist in the near future.